Dai Min, Dorjoy Md Mehedi Hassan, Miao Hong, Zhang Shanwen
College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.
Insects. 2023 Jan 5;14(1):54. doi: 10.3390/insects14010054.
Pest detection in plants is essential for ensuring high productivity. Convolutional neural networks (CNN)-based deep learning advancements recently have made it possible for researchers to increase object detection accuracy. In this study, pest detection in plants with higher accuracy is proposed by an improved YOLOv5m-based method. First, the SWin Transformer (SWinTR) and Transformer (C3TR) mechanisms are introduced into the YOLOv5m network so that they can capture more global features and can increase the receptive field. Then, in the backbone, ResSPP is considered to make the network extract more features. Furthermore, the global features of the feature map are extracted in the feature fusion phase and forwarded to the detection phase via a modification of the three output necks C3 into SWinTR. Finally, WConcat is added to the fusion feature, which increases the feature fusion capability of the network. Experimental results demonstrate that the improved YOLOv5m achieved 95.7% precision rate, 93.1% recall rate, 94.38% score, and 96.4% Mean Average Precision (). Meanwhile, the proposed model is significantly better than the original YOLOv3, YOLOv4, and YOLOv5m models. The improved YOLOv5m model shows greater robustness and effectiveness in detecting pests, and it could more precisely detect different pests from the dataset.
植物病虫害检测对于确保高产量至关重要。基于卷积神经网络（CNN）的深度学习进展最近使研究人员能够提高目标检测的准确性。在本研究中，提出了一种基于改进的YOLOv5m的方法来更准确地检测植物病虫害。首先，将SWin Transformer（SWinTR）和Transformer（C3TR）机制引入YOLOv5m网络，以便它们能够捕获更多全局特征并扩大感受野。然后，在主干网络中，采用ResSPP使网络提取更多特征。此外，在特征融合阶段提取特征图的全局特征，并通过将三个输出颈部C3修改为SWinTR将其转发到检测阶段。最后，在融合特征中添加WConcat，这提高了网络的特征融合能力。实验结果表明，改进后的YOLOv5m的精确率达到95.7%，召回率达到93.1%，得分达到94.38%，平均精度均值（mAP）达到96.4%。同时，所提出的模型明显优于原始的YOLOv3、YOLOv4和YOLOv5m模型。改进后的YOLOv5m模型在检测病虫害方面表现出更强的鲁棒性和有效性，并且能够更精确地从数据集中检测出不同的害虫。
